Optimization of type-2 fuzzy integration in ensemble neural networks for predicting the Dow Jones time series

This paper describes an optimization method based on genetic algorithms for ensemble neural networks with type-2 fuzzy integration with application to the forecasting of complex time series. The time series that was considered in this paper, to compare the hybrid genetic-neuro-fuzzy approach with traditional methods is the Dow Jones, and the results shown are for the optimization of the structure of the ensemble neural network and type-2 fuzzy integration. Simulation results show that the ensemble approach produces good prediction of the Dow Jones time series.